OMD Curation Toolkit: a workflow for in-house curation of public omics datasets.

BACKGROUND: Major advances in sequencing technologies and the sharing of data and metadata in science have resulted in a wealth of publicly available datasets. However, working with and especially curating public omics datasets remains challenging despite these efforts. While a growing number of initiatives aim to re-use previous results, these present limitations that often lead to the need for further in-house curation and processing. RESULTS: Here, we present the Omics Dataset Curation Toolkit (OMD Curation Toolkit), a python3 package designed to accompany and guide the researcher during the curation process of metadata and fastq files of public omics datasets. This workflow provides a standardized framework with multiple capabilities (collection, control check, treatment and integration) to facilitate the arduous task of curating public sequencing data projects. While centered on the European Nucleotide Archive (ENA), the majority of the provided tools are generic and can be used to curate datasets from different sources. CONCLUSIONS: Thus, it offers valuable tools for the in-house curation previously needed to re-use public omics data. Due to its workflow structure and capabilities, it can be easily used and benefit investigators in developing novel omics meta-analyses based on sequencing data.

PMBC: a manually curated database for prognostic markers of breast cancer.

Breast cancer is notorious for its high mortality and heterogeneity, resulting in different therapeutic responses. Classical biomarkers have been identified and successfully commercially applied to predict the outcome of breast cancer patients. Accumulating biomarkers, including non-coding RNAs, have been reported as prognostic markers for breast cancer with the development of sequencing techniques. However, there are currently no databases dedicated to the curation and characterization of prognostic markers for breast cancer. Therefore, we constructed a curated database for prognostic markers of breast cancer (PMBC). PMBC consists of 1070 markers covering mRNAs, lncRNAs, miRNAs and circRNAs. These markers are enriched in various cancer- and epithelial-related functions including mitogen-activated protein kinases signaling. We mapped the prognostic markers into the ceRNA network from starBase. The lncRNA NEAT1 competes with 11 RNAs, including lncRNAs and mRNAs. The majority of the ceRNAs in ABAT belong to pseudogenes. The topology analysis of the ceRNA network reveals that known prognostic RNAs have higher closeness than random. Among all the biomarkers, prognostic lncRNAs have a higher degree, while prognostic mRNAs have significantly higher closeness than random RNAs. These results indicate that the lncRNAs play important roles in maintaining the interactions between lncRNAs and their ceRNAs, which might be used as a characteristic to prioritize prognostic lncRNAs based on the ceRNA network. PMBC renders a user-friendly interface and provides detailed information about individual prognostic markers, which will facilitate the precision treatment of breast cancer. PMBC is available at the following URL: http://www.pmbreastcancer.com/.

DUVEL: an active-learning annotated biomedical corpus for the recognition of oligogenic combinations.

While biomedical relation extraction (bioRE) datasets have been instrumental in the development of methods to support biocuration of single variants from texts, no datasets are currently available for the extraction of digenic or even oligogenic variant relations, despite the reports in literature that epistatic effects between combinations of variants in different loci (or genes) are important to understand disease etiologies. This work presents the creation of a unique dataset of oligogenic variant combinations, geared to train tools to help in the curation of scientific literature. To overcome the hurdles associated with the number of unlabelled instances and the cost of expertise, active learning (AL) was used to optimize the annotation, thus getting assistance in finding the most informative subset of samples to label. By pre-annotating 85 full-text articles containing the relevant relations from the Oligogenic Diseases Database (OLIDA) with PubTator, text fragments featuring potential digenic variant combinations, i.e. gene-variant-gene-variant, were extracted. The resulting fragments of texts were annotated with ALAMBIC, an AL-based annotation platform. The resulting dataset, called DUVEL, is used to fine-tune four state-of-the-art biomedical language models: BiomedBERT, BiomedBERT-large, BioLinkBERT and BioM-BERT. More than 500 000 text fragments were considered for annotation, finally resulting in a dataset with 8442 fragments, 794 of them being positive instances, covering 95% of the original annotated articles. When applied to gene-variant pair detection, BiomedBERT-large achieves the highest F1 score (0.84) after fine-tuning, demonstrating significant improvement compared to the non-fine-tuned model, underlining the relevance of the DUVEL dataset. This study shows how AL may play an important role in the creation of bioRE dataset relevant for biomedical curation applications. DUVEL provides a unique biomedical corpus focusing on 4-ary relations between two genes and two variants. It is made freely available for research on GitHub and Hugging Face. Database URL: https://huggingface.co/datasets/cnachteg/duvel or https://doi.org/10.57967/hf/1571.

MSGD: a manually curated database of genomic, transcriptomic, proteomic and drug information for multiple sclerosis.

Multiple sclerosis (MS) is the most common inflammatory demyelinating disease of the central nervous system. 'Omics' technologies (genomics, transcriptomics, proteomics) and associated drug information have begun reshaping our understanding of multiple sclerosis. However, these data are scattered across numerous references, making them challenging to fully utilize. We manually mined and compiled these data within the Multiple Sclerosis Gene Database (MSGD) database, intending to continue updating it in the future. We screened 5485 publications and constructed the current version of MSGD. MSGD comprises 6255 entries, including 3274 variant entries, 1175 RNA entries, 418 protein entries, 313 knockout entries, 612 drug entries and 463 high-throughput entries. Each entry contains detailed information, such as species, disease type, detailed gene descriptions (such as official gene symbols), and original references. MSGD is freely accessible and provides a user-friendly web interface. Users can easily search for genes of interest, view their expression patterns and detailed information, manage gene sets and submit new MS-gene associations through the platform. The primary principle behind MSGD's design is to provide an exploratory platform, aiming to minimize filtration and interpretation barriers while ensuring highly accessible presentation of data. This initiative is expected to significantly assist researchers in deciphering gene mechanisms and improving the prevention, diagnosis and treatment of MS. Database URL: http://bio-bigdata.hrbmu.edu.cn/MSGD.

A study on formalizing the knowledge of data curation activities across different fields.

In recent years, with the trend of open science, there have been many efforts to share research data on the internet. To promote research data sharing, data curation is essential to make the data interpretable and reusable. In research fields such as life sciences, earth sciences, and social sciences, tasks and procedures have been already developed to implement efficient data curation to meet the needs and customs of individual research fields. However, not only data sharing within research fields but also interdisciplinary data sharing is required to promote open science. For this purpose, knowledge of data curation across the research fields is surveyed, analyzed, and organized as an ontology in this paper. As the survey, existing vocabularies and procedures are collected and compared as well as interviews with the data curators in research institutes in different fields are conducted to clarify commonalities and differences in data curation across the research fields. It turned out that the granularity of tasks and procedures that constitute the building blocks of data curation is not formalized. Without a method to overcome this gap, it will be challenging to promote interdisciplinary reuse of research data. Based on the analysis above, the ontology for the data curation process is proposed to describe data curation processes in different fields universally. It is described by OWL and shown as valid and consistent from the logical viewpoint. The ontology successfully represents data curation activities as the processes in the different fields acquired by the interviews. It is also helpful to identify the functions of the systems to support the data curation process. This study contributes to building a knowledge framework for an interdisciplinary understanding of data curation activities in different fields.

Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology.

The research focuses on the segmentation and classification of leukocytes, a crucial task in medical image analysis for diagnosing various diseases. The leukocyte dataset comprises four classes of images such as monocytes, lymphocytes, eosinophils, and neutrophils. Leukocyte segmentation is achieved through image processing techniques, including background subtraction, noise removal, and contouring. To get isolated leukocytes, background mask creation, Erythrocytes mask creation, and Leukocytes mask creation are performed on the blood cell images. Isolated leukocytes are then subjected to data augmentation including brightness and contrast adjustment, flipping, and random shearing, to improve the generalizability of the CNN model. A deep Convolutional Neural Network (CNN) model is employed on augmented dataset for effective feature extraction and classification. The deep CNN model consists of four convolutional blocks having eleven convolutional layers, eight batch normalization layers, eight Rectified Linear Unit (ReLU) layers, and four dropout layers to capture increasingly complex patterns. For this research, a publicly available dataset from Kaggle consisting of a total of 12,444 images of four types of leukocytes was used to conduct the experiments. Results showcase the robustness of the proposed framework, achieving impressive performance metrics with an accuracy of 97.98% and precision of 97.97%. These outcomes affirm the efficacy of the devised segmentation and classification approach in accurately identifying and categorizing leukocytes. The combination of advanced CNN architecture and meticulous pre-processing steps establishes a foundation for future developments in the field of medical image analysis.

Procedimiento para la eliminación de documentos administrativos institucionales / Procedure for the elimination of institutional administrative documents

Los presentes Lineamientos tiene como objetivo, estandarizar las actividades a cumplir por el personal responsable de la eliminación de documentos administrativos para volver eficiente el procedimiento y reducir costos de conservación. Están sujetos al cumplimiento del presente procedimiento, todas las dependencias del Minsal y Establecimientos de Salud que generan archivos institucionales de valor primario (administrativo), iniciando con la identificación de la documentación que ya cumplió su plazo de conservación establecido en la TPCD, concluyendo con la destrucción de la documentación

The objective of these Guidelines is to standardize the activities to be carried out by the personnel responsible for the elimination of administrative documents to make the procedure efficient and reduce conservation costs. All Minsal agencies and Health Establishments that generate institutional files of primary (administrative) value are subject to compliance with this procedure, starting with the identification of documentation that has already met its conservation period established in the TPCD, concluding with the destruction of documentation

BioKC: a collaborative platform for curation and annotation of molecular interactions.

Curation of biomedical knowledge into systems biology diagrammatic or computational models is essential for studying complex biological processes. However, systems-level curation is a laborious manual process, especially when facing ever-increasing growth of domain literature. New findings demonstrating elaborate relationships between multiple molecules, pathways and cells have to be represented in a format suitable for systems biology applications. Importantly, curation should capture the complexity of molecular interactions in such a format together with annotations of the involved elements and support stable identifiers and versioning. This challenge calls for novel collaborative tools and platforms allowing to improve the quality and the output of the curation process. In particular, community-based curation, an important source of curated knowledge, requires support in role management, reviewing features and versioning. Here, we present Biological Knowledge Curation (BioKC), a web-based collaborative platform for the curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML). BioKC offers a graphical user interface for curation of complex molecular interactions and their annotation with stable identifiers and supporting sentences. With the support of collaborative curation and review, it allows to construct building blocks for systems biology diagrams and computational models. These building blocks can be published under stable identifiers and versioned and used as annotations, supporting knowledge building for modelling activities.

Approaching Mass Cytometry Translational Studies by Experimental and Data Curation Settings.

Clinical studies are conducted to better understand the pathological mechanism of diseases and to find biomarkers associated with disease activity, drug response, or outcome prediction. Mass cytometry (MC) is a high-throughput single-cell technology that measures hundreds of cells per second with more than 40 markers per cell. Thus, it is a suitable tool for immune monitoring and biomarker discovery studies. Working in translational and clinical settings requires a careful experimental design to minimize, monitor, and correct the variations introduced during sample collection, preparation, acquisition, and analysis. In this review, we will focus on these important aspects of MC-related experiments and data curation in the context of translational clinical research projects.

ClinGen variant curation expert panel recommendations for classification of variants in GAMT, GATM and SLC6A8 for cerebral creatine deficiency syndromes.

Cerebral creatine deficiency syndromes (CCDS) are inherited metabolic phenotypes of creatine synthesis and transport. There are two enzyme deficiencies, guanidinoacetate methyltransferase (GAMT), encoded by GAMT and arginine-glycine amidinotransferase (AGAT), encoded by GATM, which are involved in the synthesis of creatine. After synthesis, creatine is taken up by a sodium-dependent membrane bound creatine transporter (CRTR), encoded by SLC6A8, into all organs. Creatine uptake is very important especially in high energy demanding organs such as the brain, and muscle. To classify the pathogenicity of variants in GAMT, GATM, and SLC6A8, we developed the CCDS Variant Curation Expert Panel (VCEP) in 2018, supported by The Clinical Genome Resource (ClinGen), a National Institutes of Health (NIH)-funded resource. We developed disease-specific variant classification guidelines for GAMT-, GATM-, and SLC6A8-related CCDS, adapted from the American College of Medical Genetics/Association of Molecular Pathology (ACMG/AMP) variant interpretation guidelines. We applied specific variant classification guidelines to 30 pilot variants in each of the three genes that have variants associated with CCDS. Our CCDS VCEP was approved by the ClinGen Sequence Variant Interpretation Working Group (SVI WG) and Clinical Domain Oversight Committee in July 2022. We curated 181 variants including 72 variants in GAMT, 45 variants in GATM, and 64 variants in SLC6A8 and submitted these classifications to ClinVar, a public variant database supported by the National Center for Biotechnology Information. Missense variants were the most common variant type in all three genes. We submitted 32 new variants and reclassified 34 variants with conflicting interpretations. We report specific phenotype (PP4) using a points system based on the urine and plasma guanidinoacetate and creatine levels, brain magnetic resonance spectroscopy (MRS) creatine level, and enzyme activity or creatine uptake in fibroblasts ranging from PP4, PP4_Moderate and PP4_Strong. Our CCDS VCEP is one of the first panels applying disease specific variant classification algorithms for an X-linked disease. The availability of these guidelines and classifications can guide molecular genetics and genomic laboratories and health care providers to assess the molecular diagnosis of individuals with a CCDS phenotype.

scDOT: enhancing single-cell RNA-Seq data annotation and uncovering novel cell types through multi-reference integration.

The proliferation of single-cell RNA-seq data has greatly enhanced our ability to comprehend the intricate nature of diverse tissues. However, accurately annotating cell types in such data, especially when handling multiple reference datasets and identifying novel cell types, remains a significant challenge. To address these issues, we introduce Single Cell annotation based on Distance metric learning and Optimal Transport (scDOT), an innovative cell-type annotation method adept at integrating multiple reference datasets and uncovering previously unseen cell types. scDOT introduces two key innovations. First, by incorporating distance metric learning and optimal transport, it presents a novel optimization framework. This framework effectively learns the predictive power of each reference dataset for new query data and simultaneously establishes a probabilistic mapping between cells in the query data and reference-defined cell types. Secondly, scDOT develops an interpretable scoring system based on the acquired probabilistic mapping, enabling the precise identification of previously unseen cell types within the data. To rigorously assess scDOT's capabilities, we systematically evaluate its performance using two diverse collections of benchmark datasets encompassing various tissues, sequencing technologies and diverse cell types. Our experimental results consistently affirm the superior performance of scDOT in cell-type annotation and the identification of previously unseen cell types. These advancements provide researchers with a potent tool for precise cell-type annotation, ultimately enriching our understanding of complex biological tissues.

Image annotation and curation in radiology: an overview for machine learning practitioners.

"Garbage in, garbage out" summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train and validate models should indeed be consistent, standardised, traceable, correctly annotated, and de-identified, considering local regulations. This narrative review presents a summary of the techniques that are used to ensure that all these requirements are fulfilled, with special emphasis on radiological imaging and freely available software solutions that can be directly employed by the interested researcher. Topics discussed include key imaging concepts, such as image resolution and pixel depth; file formats for medical image data storage; free software solutions for medical image processing; anonymisation and pseudonymisation to protect patient privacy, including compliance with regulations such as the Regulation (EU) 2016/679 "General Data Protection Regulation" (GDPR) and the 1996 United States Act of Congress "Health Insurance Portability and Accountability Act" (HIPAA); methods to eliminate patient-identifying features within images, like facial structures; free and commercial tools for image annotation; and techniques for data harmonisation and normalisation.Relevance statement This review provides an overview of the methods and tools that can be used to ensure high-quality data for machine learning and artificial intelligence applications in radiology.Key points• High-quality datasets are essential for reliable artificial intelligence algorithms in medical imaging.• Software tools like ImageJ and 3D Slicer aid in processing medical images for AI research.• Anonymisation techniques protect patient privacy during dataset preparation.• Machine learning models can accelerate image annotation, enhancing efficiency and accuracy.• Data curation ensures dataset integrity, compliance, and quality for artificial intelligence development.

Community recommendations on cryoEM data archiving and validation.

In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for the deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 47 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and the resulting consensus recommendations. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations.

Creating a Medication Therapy Observational Research Database from an Electronic Medical Record: Challenges and Data Curation.

BACKGROUND: Observational research has shown its potential to complement experimental research and clinical trials by secondary use of treatment data from hospital care processes. It can also be applied to better understand pediatric drug utilization for establishing safer drug therapy. Clinical documentation processes often limit data quality in pediatric medical records requiring data curation steps, which are mostly underestimated. OBJECTIVES: The objectives of this study were to transform and curate data from a departmental electronic medical record into an observational research database. We particularly aim at identifying data quality problems, illustrating reasons for such problems and describing the systematic data curation process established to create high-quality data for observational research. METHODS: Data were extracted from an electronic medical record used by four wards of a German university children's hospital from April 2012 to June 2020. A four-step data preparation, mapping, and curation process was established. Data quality of the generated dataset was firstly assessed following an established 3 × 3 Data Quality Assessment guideline and secondly by comparing a sample subset of the database with an existing gold standard. RESULTS: The generated dataset consists of 770,158 medication dispensations associated with 89,955 different drug exposures from 21,285 clinical encounters. A total of 6,840 different narrative drug therapy descriptions were mapped to 1,139 standard terms for drug exposures. Regarding the quality criterion correctness, the database was consistent and had overall a high agreement with our gold standard. CONCLUSION: Despite large amounts of freetext descriptions and contextual knowledge implicitly included in the electronic medical record, we were able to identify relevant data quality issues and to establish a semi-automated data curation process leading to a high-quality observational research database. Because of inconsistent dosage information in the original documentation this database is limited to a drug utilization database without detailed dosage information.

Landmark annotation through feature combinations: a comparative study on cephalometric images with in-depth analysis of model's explainability.

OBJECTIVES: The objectives of this study are to explore and evaluate the automation of anatomical landmark localization in cephalometric images using machine learning techniques, with a focus on feature extraction and combinations, contextual analysis, and model interpretability through Shapley Additive exPlanations (SHAP) values. METHODS: We conducted extensive experimentation on a private dataset of 300 lateral cephalograms to thoroughly study the annotation results obtained using pixel feature descriptors including raw pixel, gradient magnitude, gradient direction, and histogram-oriented gradient (HOG) values. The study includes evaluation and comparison of these feature descriptions calculated at different contexts namely local, pyramid, and global. The feature descriptor obtained using individual combinations is used to discern between landmark and nonlandmark pixels using classification method. Additionally, this study addresses the opacity of LGBM ensemble tree models across landmarks, introducing SHAP values to enhance interpretability. RESULTS: The performance of feature combinations was assessed using metrics like mean radial error, standard deviation, success detection rate (SDR) (2 mm), and test time. Remarkably, among all the combinations explored, both the HOG and gradient direction operations demonstrated significant performance across all context combinations. At the contextual level, the global texture outperformed the others, although it came with the trade-off of increased test time. The HOG in the local context emerged as the top performer with an SDR of 75.84% compared to others. CONCLUSIONS: The presented analysis enhances the understanding of the significance of different features and their combinations in the realm of landmark annotation but also paves the way for further exploration of landmark-specific feature combination methods, facilitated by explainability.

Learning Without Real Data Annotations to Detect Hepatic Lesions in PET Images.

OBJECTIVE: Deep neural networks have been recently applied to lesion identification in fluorodeoxyglucose (FDG) positron emission tomography (PET) images, but they typically rely on a large amount of well-annotated data for model training. This is extremely difficult to achieve for neuroendocrine tumors (NETs), because of low incidence of NETs and expensive lesion annotation in PET images. The objective of this study is to design a novel, adaptable deep learning method, which uses no real lesion annotations but instead low-cost, list mode-simulated data, for hepatic lesion detection in real-world clinical NET PET images. METHODS: We first propose a region-guided generative adversarial network (RG-GAN) for lesion-preserved image-to-image translation. Then, we design a specific data augmentation module for our list-mode simulated data and incorporate this module into the RG-GAN to improve model training. Finally, we combine the RG-GAN, the data augmentation module and a lesion detection neural network into a unified framework for joint-task learning to adaptatively identify lesions in real-world PET data. RESULTS: The proposed method outperforms recent state-of-the-art lesion detection methods in real clinical 68Ga-DOTATATE PET images, and produces very competitive performance with the target model that is trained with real lesion annotations. CONCLUSION: With RG-GAN modeling and specific data augmentation, we can obtain good lesion detection performance without using any real data annotations. SIGNIFICANCE: This study introduces an adaptable deep learning method for hepatic lesion identification in NETs, which can significantly reduce human effort for data annotation and improve model generalizability for lesion detection with PET imaging.

Registro español de marcapasos. XX informe oficial de la Asociación del Ritmo Cardiaco de la Sociedad Española de Cardiología (2022) / Spanish pacemaker registry. 20th official report of the Heart Rhythm Association of the Spanish Society of Cardiology (2022)

Introducción y objetivos: En este informe se comunica la actividad de estimulación cardiaca en 2022: número total de implantes, adherencia a la monitorización a distancia, factores demográficos y clínicos y características del material implantado. Métodos: Las fuentes de información son la plataforma CardioDispositivos, la tarjeta europea del paciente portador de marcapasos y los datos facilitados por los fabricantes. Resultados: Las tasas de marcapasos convencionales y resincronizadores de baja energía fueron de 866 y 34 unidades/millón respectivamente. Se implantaron 815 marcapasos sin cables. Se registraron 16.426 procedimientos de 82 hospitales (9.407 a través de CardioDispositivos), lo que supone un 40% de la actividad. La media de edad fue 78,6 años, con predominio de varones (60,3%). El bloqueo auriculoventricular fue el trastorno más frecuente y el 14,5% de los pacientes estaban en fibrilación auricular. Predomina el modo de estimulación DDD/R (55,6%) y la edad influye en el modo de estimulación, de forma que más de un tercio de los pacientes mayores de 80 años en ritmo sinusal recibieron estimulación monocameral en ventrículo. Se incluyeron en monitorización a distancia el 35% de los marcapasos y el 55% de los resincronizadores de baja energía. Conclusiones: Aumentan en un 5,6% el número de marcapasos convencionales, un 16% los resincronizadores de baja energía y un 25% los marcapasos sin cables. Se estabiliza la adherencia a la monitorización a distancia. Aumenta en un 11% el número de procedimientos incluidos en CardioDispositivos, aunque disminuye el volumen de muestra. El uso extensivo de la plataforma es lo que permitirá en años venideros contar con un registro de calidad.(AU)

Introduction and objectives: This article reports the cardiac pacing activity performed in 2022, including the total number of implants, adherence to remote monitoring, demographic and clinical factors, and the characteristics of the implanted devices. Methods: The information sources were the CardioDispositivos online platform, the European pacemaker patient identification card, and data provided by the manufacturers. Results: The rates of conventional pacemakers and low-energy resynchronizers were 866 and 34 units per million population, respectively. A total of 815 leadless pacemakers were implanted. In all, 16426 procedures performed in 82 hospitals were reported (9407 through CardioDispositivos), representing 40% of the activity. The mean age was 78.6 years, with a predominance of men (60.3%). The most frequent disorder was atrioventricular block, and 14.5% of the patients had atrial fibrillation. There was a predominance of the DDD/R pacing mode (55.6%), and pacing mode was influenced by age, such that more than one-third of patients older than 80 years in sinus rhythm received single-chamber ventricular pacing. The remote monitoring program included 35% of conventional pacemakers and 55% of low-energy resynchronization pacemakers. Conclusions: The number of conventional pacemakers increased by 5.6%, low-energy resynchronizers by 16%, and leadless pacemakers by 25%. Adherence to remote monitoring was stable. The number of procedures included in CardioDispositivos increased by 11%, although the sample volume decreased. In the coming years, the widespread use of the platform will likely lead to a high-quality registry.(AU)